For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

We study stochastic processes that generate nongrowing complex networks without self-loops and multiple edges (simple graphs). The work concentrates on understanding and formulation of constraints which keep the rewiring stochastic processes within the class of simple graphs. To formulate these constraints a different concept of wedge distribution (paths of length 2) is introduced and its relation to degree-degree correlation is studied. The analysis shows that the constraints, together with edge selection rules, do not even allow the formulation of a closed master equation in the general case. We also introduce a particular stochastic process which does not contain edge selection rules, but which, we believe, can provide some insight into the complexities of simple graphs.

In the case of extracellular recordings, spike detection algorithms are necessary in order to retrieve information about neuronal activity form the data. We present a new spike detection algorithm which is based on methods from the field of blind equalization and beamforming. In contrast to existing approaches, our method estimates several waveforms directly from the data and corresponding linear filters are constructed. The estimation is done in an unsupervised manner, and the few parameters of the algorithm are intuitive to set. The algorithm allows for superior detection performance, even when multiple neurons with various waveforms are present in the data. We compare our method with current state-of-the-art spike detection algorithms, and show that the proposed method achieves favorable results.

Extracellular recordings are a key tool to record the activity of neurons in vivo. Especially in the case of experiments with behaving animals, however, the tedious procedure of electrode placement can take a considerable amount of expensive and restricted experimental time. Furthermore, due to tissue drifts and other sources of variability in the recording setup, the position of the electrodes with respect to the recorded neurons can change causing low recording quality. The contributions of this work are threefold. We introduce a quality measure for the recording position of the electrode which should be maximized during recordings and is especially suitable for the use of multi-electrodes. An automated positioning system based on this quality measure is proposed. The system is able to find favorable recording positions and adapts the electrode position smoothly to changes of the neuron positions. Finally, we evaluate the system using a new simulator for extracellular recordings based on realistically reconstructed 3D neurons.